Manufacturing Operations Management Talk

Clarifying the New Manufacturing IT Lingo

- Smart Manufacturing vs Industrie 4 vs Digital Manufacturing vs IIoT

I had the pleasure of facilitating a workshop on Smart Manufacturing at the Industry Week conference with over 120 professionals involved in different aspects of manufacturing information technology (IT) in a wide range of industries. After we briefly introduced Smart Manufacturing and shared a few results from a MESA-SCMWorld survey, I casually surveyed the group on clarity
among the new terms in manufacturing IT and adoption for the “plug and play” paradigms promoted by Industrial Internet of Things (IIoT), Smart Manufacturing, Digital Manufacturing, and Industrie 4.0 initiatives. Some of my observations from this interesting discussion follow.

QUESTION: Third or Fourth Industrial Revolution?

SURVEY SAYS: 4TH Industrial Revolution. The audience was pretty confident with this response. There was an overwhelming show of hands in favor of 4th versus 3rd.

Some consulting groups have favored calling this era the start of the third industrial revolution and others are calling it the fourth industrial revolution. Everyone agrees that the era of mass production is behind us. An era that was enabled by revolutionary advances like the assembly line and electrical standards. But many groups closer to manufacturing automation want to recognize that there was a third industrial revolution that started in the 1970’s fueled by advances in industrial automation and software for personal computers (PCs).

There is agreement that the next industrial revolution is starting now, and it is fueled by advances in model-based manufacturing, additive manufacturing, robotics, and the Internet of Things (IoT). New technologies are allowing manufacturers to move from mass production make-to-stock practices towards make-to-order mass customization business models. Based on popular opinion, we should stick to calling this the 4th Industrial Revolution.

QUESTION: IoT and IIoT side by side or one under the other?

Do we view the Industrial Internet of Things (IIoT) as a subset of the Internet of Things (IoT) with similar standards and methods of connecting manufacturing, home and office devices to cloud applications and smart phones? Or do we see IoT and IIoT as having similar goals but using different standards and infrastructure?

SURVEY SAYS: IIoT should be subset of IoT. The majority of manufacturers want to see industrial automation use similar standards and mechanism to home and office equipment integration. They would like to see apps on their phones with the ability to view, interact and control the shop just like they have apps today controlling their home or car. IIoT standards and methods should be a subset of IoT methods and standards.

One automation vendor warned that this view of IIoT and IoT might be an oversimplified view on the subject. Point taken, but the survey was more about confirming the expectations of these initiatives and the desires of the end users—similar type of “plug and play” capabilities at the factory and at home.

Another voice wanted to claim that many companies are already doing IIoT at their plants. We went ahead and tested this claim with the audience.

QUESTION: How many companies in this audience currently have integration from
machines to enterprise systems?

SURVEY SAYS: Around 80% of hands were up.

QUESTION: How many of these companies are using open standards for integration
and could simply swap out one automation or software vendor for another?

SURVEY SAYS: Only one timid person in the audience raised their hand half way
up.

These answers point to a big difference between IIoT and prior manufacturing automation integration methods. For the last few decades we depended on custom integration, proprietary interfaces and separate protocols for integration and automation at the factory. Moving forward with IIoT we want to embrace open standards and internet protocols so we can easily swap and mix multi-vendor equipment and software which might be on-premise or in the cloud.

QUESTION: Are Smart Manufacturing and Industrie 4.0 the same thing?

SURVEY SAYS: Yes. The consensus was that in general they are close enough in scope and purpose. However, Industrie 4.0 is perceived as a very specific German initiative so the majority of participants preferred the term Smart Manufacturing.

It is important to note that if initiatives from different countries do not coordinate among each other, we could end up with different standards supported by different countries. Similar to how we ended up with slightly different electrical standards in different countries. Nevertheless, better off with a few standards than none at all and we can always use converters to plug equipment to
different standards.

I noticed that the general voting participation was lower on this topic so I paused the survey for a few clarifications.

QUESTION: Would you like more clarification on the scope of Smart Manufacturing?

SURVEY SAYS: Yes.

Smart Manufacturing is about creating smart products, smart factories, smart manufacturing processes and smart enterprise procedures that link the entire product value chain with a digital thread.

Smart factories are the foundation of Smart Manufacturing. Smart machines and robots in smart factories are capable of managing complexity, are less prone to disruption and are able to manufacture goods more efficiently. In the smart factory, human beings, machines and resources communicate with each other as naturally as in a social network.

Smart products know about their configuration, details of how they were manufactured including critical components and how they are intended to be used. They actively support the anufacturing process, answering questions such as “which parameters should be used to process me?”, “when was I made?”, “for which customer?”, and “when do I need to be serviced?”

QUESTION: Should we view Digital Manufacturing as part of Smart Manufacturing or should we view Digital and Smart as separate initiatives?

SURVEY SAYS: Not clear. So instead we further discussed the difference and relationship between smart and digital manufacturing.

The digital thread that starts with the 3D model-based definition of the product from engineering flows to smart manufacturing and to the smart supply chain linked via standard integration interfaces that connect to web applications, mobile devices and cloud services. The network of connected devices, resources systems, partners and suppliers will result in the transformation of
conventional value chains and the emergence of new business models.

Several industries have used the term Model-Based Enterprise for the initiative of creating a continuous digital thread from design 3D models and specifications (a.k.a. Model-Based Engineering) flowing downstream into the supply chain, manufacturing, inspection and aftermarket services of the product.

As stated earlier, Smart Manufacturing includes smart products and smart manufacturing processes. The digital thread must be interpreted by smart product and smart manufacturing processes in order to build to the right product specifications and configuration. Therefore, we see the digital thread as a requirement for smart manufacturing. We also agree that these two initiatives could be worked side by side if the intersection areas and integration is coordinated among the two initiatives and well defined.

QUESTION: Are IIoT and Smart Manufacturing hype or substance?

SURVEY SAYS: Hype with opportunity behind it. The participants believe there is a bit of hype in the media right now but they also believe there is opportunity for big improvements in manufacturing based on the adoption of new technologies and standards into new manufacturing business models. The intent of these initiatives resonate with the audience.

Did I clarify things? Are we smarter now? (Trick question)

Thanks to everyone that participated in the workshop, especially to those who “volunteered” to participate : )

Are Your Manufacturing Operations Ready for Transparency?

What does transparency mean in the context of manufacturing operations? Literally it means we can see through the manufacturing organization into its internal workings. Practically it means full availability and access to information required for collaboration and collective management decision making. It means full disclosure of detailed historical records for root-cause investigation purposes to uncover areas for continuous improvement. It means being open to share our best practices along with our past mistakes so they serve as learning experiences.

Transparency is an essential condition for a free and open exchange and evaluation of priorities for process improvements. It is essential to creating a culture of trust and teamwork in the organization.

Transparency through metrics

The deployment of a performance measurement system with standardized manufacturing metrics is a key ingredient to providing unbiased visibility and transparency within the organization.

In addition to providing a mechanism for transparency, carefully selected metrics can help an organization achieve its strategic goals. Work groups can become more motivated and engaged when their performance is measured according to well selected numeric metrics. Personnel must understand the relation of their metrics to the corporate goals and feel that the targets are achievable. Work group level metrics are considered a better practice than individual employee goals to promote teamwork in the organization. Wouldn’t it be great if all employees understood the corporate goals and how they could make a difference?

Personnel must trust the accuracy of the data and the calculation of the metrics. Manually calculated metrics in spreadsheets can lead to introduction of bias, manipulation of the data and general distrust of the reported numbers. Having a manufacturing system that automatically collects data and rolls it up into metrics avoids these types of concerns and greatly improves accuracy.

Metrics selection

The selection of good metrics and the involvement of key stakeholders can greatly help with the trust and adoption of the standardized measurement system.

Generally we should be able to relate the metric to (a) strategic corporate goals, and (b) to desired outcomes for the customer. If the relation between the metric and a corporate goal or key performance indicator (KPI) is indirect, we should explain and publish this relation so it is very clear to all stakeholders.

It should be fairly straightforward to understand how the metric is derived. This will make it easier for everyone to visualize, trust and relate to the metric.

The consequences of bad performance of the metric should be easy to see on the shop floor. For example, for longer than planned cycle times we should start seeing work orders and planned inventory queuing up somewhere in the work cell. For an increase in cost of poor quality, we should see an increase of rework orders at the shop floor.

The metric should be actionable. It should be easy to derive the types of actions that could be taken to improve performance in relation to the metric. For example, a metric should not combine too many measures into one number because the possible corrective action becomes very unclear.

Metrics should be consistent and independent of subjective bias. A good metric is consistent in what it measures. The source of data, the equations used, and the weighting criteria applied should be consistent but might also evolve over time. When it does change, it should be communicated to all stakeholders. It is good practice to avoid measures where a subjective evaluation is introduced in the calculation. Different people might apply the subjective criteria differently leading to inconsistent results for that metric in the organization.

Accountability and fairness

The organization can set goals to achieve specific target levels on key metrics within a certain timeframe to promote motivation, create urgency, and establish commitments for the team. Accountability in the organization is driven by how performance to the goals is tied to a performance reward system and the actions taken by the management team about poor performance.

For accountability to work, personnel must understand and trust the metrics, perceive the targets to be achievable, and feel that they are empowered to improve performance on the metrics selected for goals. For example, manufacturing throughput rates are constrained by the number of purchase orders brought in from Sales. Increasing the number of purchase orders is out of the control of Operations so it would be better to set performance goals for Operations on something they have more control on like improving cycle times.

In order to successfully set goals and reward systems based on metrics, they must be fair across the organization. But some types of work are harder and more prone to error than others. To be fair with metrics used to compare and benchmark across the organization, it is important to have a method to weigh the difference in difficulty for achieving similar results across the different types of work centers. An example of a weighted metric is the Six Sigma metric, Defects Per Million Opportunities (DPMO). Since DPMO weighs defects by the number of opportunities for defects in each manufacturing operation, we can use the metric to compare performance across different work centers.

Constructive scrutiny

Are you ready for peers looking over your shoulder? Questioning your numbers? Are you used to constructive feedback? The executive management team must be open to bad news and reward the messenger of bad news. Instead of punishing the manager that comes forward, the organization should provide additional budget to that department to help fix the problems. Other managers will get the idea and embrace honest reporting as part of the organization’s continuous improvement processes.

I heard Allan Mulally a few years ago (CEO at Ford at the time) talking about establishing a culture of teamwork and rewarding bad news in management meetings.  Alan celebrated finding improvement opportunities. When he started at Ford, he would ask “How can all the lights in the dashboards be all green when we just announced big losses?” He applauded the manager that brought that first issue up in a meeting. Instead of frustration, he showed enthusiasm and asked, "How can we help you solve that problem?" The following week, he was presented with a rainbow of colors in the charts.

The anxiety can be high in management meetings when there is so much visibility and transparency. It is important to keep the meetings positive with a spirit to collectively address problems and improve the overall organization.

Team buy in

Often it is easier to select a set of metrics than to get employees to buy into the performance measurement system. We listed some important considerations above to improve stakeholder acceptance and trust. Personnel that is not used to being evaluated based on metrics will offer a natural resistance to this new process. The executive management team must express a compelling case for how this new system is linked to the organization achieving its strategic goals.

A few key departments can be selected to pilot the new system. Change champions should be enlisted in these departments and early success in those departments with the new system should have a high visibility celebration.

Evolution over time

Just as a company’s goals and objectives evolve over time so should the set of performance metrics be revisited periodically and changed over time.

For example, for the last two years the strategic goals might have been to reduce cost by 10% and metrics were selected to reduce cost of poor quality and improve labor efficiency. This year the corporation might have a growth strategy and it wants to focus on reducing cycle time and time to introduce engineering changes into production.

Deploying a standard performance measurement system is no simple task but it is a tool to help your organization move up to a new level of business process maturity. It can become a differentiator for your business, and lead to major improvements to the bottom line.

 

Do We Have Manufacturing Problems Looking for Smart Solutions?

If you are responsible for manufacturing operations improvements, you can probably dream of a few solutions that would make some hard production problems go away or take your operations to a new level of efficiency. When you hear titles like “Smart Manufacturing”, it is easy to hope that these initiatives will yield some of those dream solutions.

I was in brainstorming workshops a few weeks ago discussing the next generation of platforms and integrated systems for Smart Manufacturing. The discussions around potential new enhanced processes were very fructiferous, so I am sharing a few ideas I personally found very interesting.

• Manufacturing machines could set up themselves and select the right configuration files based on auto identifying the product’s configuration from an RFID tag. 
• More defects could be detected in-process through robotic CMM that read the product configuration RFID, read the latest product definition directly out of CAD files, and automatically measure critical dimensions as the material is cut or product is assembled. 
• Processes that depend on final product testing to ensure product quality can cause a lot of scrap waste when the final product does not meet specifications. It would be more efficient to have improved sensors that would monitor the product recipe as ingredients are added and transformed with feedback processes that would adjust the process automatically to account for material, process or environment variables. 
• Variation within control specs/limits of raw material properties causes shifts in final product quality. We want to auto adjust our process or recipe to accommodate variations in materials instead of having to demand costly higher accuracy in material specifications. Material would come with actual measured properties instead of us assuming that they meet a specification. 
• Employee safety could be improved by using sensors and automating test procedures involving hazardous materials or processes.

It is easy to see that these types of processes would yield many benefits including:

• Quality consistency of realized products with less waste/scrap and rework
• Less labor of non-value added tasks like setup, testing, and fixing 
• Lower cost for manufacturing one custom configured piece at a time
• Improved personnel safety

But is it possible to reduce the cost of achieving these levels of automation? Will “Smart Manufacturing” ideas and new technologies get us there? Or will it always be easier to design human sensing and decision making into these processes? Some of the challenges
to achieving these levels of automation include:

• High cost of sensor and analysis technology 
• Lack of integration standards between hardware, network and software tiers 
• Lack of models that link available measurements/inputs to desired output properties
• Lack of trained resources in the factory to implement new technology

Can we can make these technologies more affordable and easier to implement. If we can, what problems would you want to address in your factory with the new generation of Smart Manufacturing technologies and integration standards?

 

Where Do Manufacturing Strategists get their Information?

 A German survey showed that strategists working on Industrie 4 and related initiatives like IIoT and Smart Manufacturing are getting their information this way: 

 

These numbers probably also apply to Manufacturing IT professionals working on manufacturing operations management systems. Where do you get your information? 

Reference: 

"How far are we? Industry 4.0", hannovermess.de whitepaper, 2015

 

 

Future Smart Manufacturing and IIoT Systems will Leverage Cloud Services

A little background on cloud services

Before diving into the manufacturing system specific topic, it is necessary to clarify the term “cloud
services”. There are multiple cloud platform models in use today but this article will focus on the
opportunities provided by leveraging new commercial cloud component services in a mix with on-premise software versus looking at moving the entire information system to the “cloud”.

Types of cloud services:
• Infrastructure-as-a-Service (IaaS) – Virtual machine services accessed over the network providing
computational and storage capabilities
• Software-as-a-Service (SaaS) – Software applications (such as PLM, ERP, Inventory Control, or CRM)
provided as a service from the cloud
• Platform-as-a-Service (PaaS) - Platform software services (including application server, database
server, web user interface framework, workflow orchestration services, and enterprise service bus
middleware) on which to build custom web service-based applications for the enterprise
• Component-as-a-Service (CaaS) - Commercial cloud delivered technology, data and connectivity services (such as identity verification, app marketplace, supply chain data exchanges, analytical engines and document storage) that can be incorporated into on-premise or cloud-based custom enterprise applications. In contrast to regular web sites focused on interacting with human users, these cloud component services provide APIs to exchange data directly with applications.

From a financial point of view these cloud services can be very exciting because they are provided
under term and usage pricing models. But in addition to the new pricing models that are making new technology adoption more affordable, we should also get excited about the new functionality and integration options enabled.

The benefits of using SaaS and IaaS have been covered in many general information technology articles, but I haven’t seen much discussion on the potential for PaaS and CaaS for manufacturing systems. I also want to propose that a mixed model leveraging a combination of distributed on-premise applications and cloud component services should provide the best framework for future manufacturing systems.

Cloud services and new standards evolving around the Industrial Internet of Things (IIoT) are rendering the old division between automation, IT and business obsolete. The availability of quality cloud component services that are easy to assemble into a custom application will change the future of the Information Technology (IT) department. Other enterprise departments will be able to assemble applications they need themselves on a UI, workflow and integration framework. The IT department will change from focus on developing, installing and supporting software applications, to focus on providing guidelines and a framework for assembling integrated enterprise applications. The framework provided by the IT department will include security schemes, brokering of the mix of commercial IaaS, SaaS, PaaS and CaaS, and integration standards that the enterprise will use.

The question should not be whether or not our manufacturing system will be on the cloud, but instead figuring out how much of our manufacturing system will be on the cloud to maximize the benefits to our organizations and customers. Cloud computing is here to stay providing new software delivery models and information technology options. A mix of on-premise plus on-cloud solutions will most likely provide the best landscape for future manufacturing systems, and in fact some software vendors are already moving in that direction. Business critical data, and speed/availability sensitive applications can be maintained on-premise. Integrated enterprise systems can leverage a layered and distributed landscape of on-premise applications and on-cloud components integrated via SOA (service-oriented architecture) interfaces. The new IT options enabled by cloud services will become more affordable and easier to maintain which will be of great interest to smaller organizations with a smaller IT staff.

A hybrid on-premise and on-cloud model for manufacturing systems

What would this mixed on-premise and on-cloud model look like? I could see it having: (a) an enterprise level business process management (BPM) and workflow orchestration engine, (b) integration mechanisms to enable receiving and sending information between the BPM layer and other layers from machine to plant to supply chain, (c) several persistent layers of data with different precision and time horizons for data needed at the controller level versus business level, and (d) a combination of role specific and enterprise system applications designed to optimize the job of each user role and function in the overall integrated enterprise system.

 

Manufacturing systems orchestrate interdepartmental processes

The Manufacturing Operations department cannot effectively be managed as an island isolated from other enterprise departments including Engineering, Supplier Management, Quality Management, Human Resources, Facilities Management and Financial Management. We need effective ways to create information threads for complete business processes across departments that do not depend on manual translation of information. We currently run many interdepartmental business process via paper, email, and with many manual interpretations and translations of data inputs to outputs along the way. These manual interdepartmental business processes are error prone and cannot scale to handle higher volume of transactions.

Business Process Management (BPM) and Workflow tools can be used to string together business processes that span across enterprise departments. For example, managing the release of a revision to a product design from engineering to supply chain to revise component parts, to quality to revise inspection requirements, to fabrication to revise NC programs, and to production to revise assembly procedures. All these processes need to be orchestrated so that they cut in at the right time so the right version of the components are available for production with the right machine and inspection instructions.

An ideal manufacturing system platform would facilitate (a) integration throughout different functional layers including automation control, operations management, equipment maintenance, supply logistics, and business management layers, and (b) integration throughout parallel management systems including physical, human, information, sustainability, economic, social, and regulatory systems.

Cloud component services for manufacturing systems

Types of cloud component services that could be leveraged in a manufacturing system include:
• Data Services including Data Storage, Data Format Translation, Document Repository
• Analysis Services including statistical correlation or predictive analysis that does not have to run
real-time along with the run of a machine process
• Specialized Apps including Document Authoring and File Viewers for specific CAD formats
• Industry Market Places to acquire parts and materials more effectively
• Supply Chain Data Exchanges for controlled communications tied to contracts between multiple tiers of suppliers in the product value chain
• Weather Services to optimize energy utilization settings at the factory
• Product Transportation and Distribution Hubs to get the product to the distribution channel and
customer in the most efficient manner
• Identity Verification Services to make sure the person or machine trying to exchange information is authorized to access the information requested.
• Printing Services in case you must still print something at the corner printing store : )

Distributed layered manufacturing system between business and machines

Could we just move the whole manufacturing system to the cloud? Perhaps for some cases, however at some plants more intimate controller-machine interactions should probably remain on-premise near the machine. For a more distributed and layered system, the general MOM (Manufacturing Operations Management) functions of Work Order Management, WIP Management, Quality, Maintenance, etc. could be provided by applications hosted in a cloud service that would interface to multiple lower level “Smart Machine Controller Appliances” at the shop floor.

The Internet of Things (IoT) interconnects “smart” devices via standard Internet Protocol Suite
(TCP/IP). The Industrial Internet of Things (IIoT) has to strive for the same goal for a new generation of “smart” machines. New IIoT standards should not maintain the old networking standards (Profibus, Fieldbus, etc) used for older machine automation controls. To achieve this goal we might need an intermediary application layer that will translate “smart” communications to older “not as smart” controllers, machines and sensors that do not speak the new integration language standards directly.

In the IIoT, the proliferation of connected smart machines, devices, and sensors can result in an
explosion of data. A distributed layered system is a way to contend with this explosion and organize
people-to-people connections, people-to-machine, and machine-to-machine communications. If
organizations embrace this opportunity to organize these automated communications and leverage all the resulting data at different layers, the visibility and analysis enabled will lead to new levels of
visibility, capabilities, accuracy, and control.

A Smart Machine Controller Appliance would be placed on-premise closer to the machines it controls for any of the following reasons:

(a) The controller function needs high-availability and high-speed connectivity
(b) The controller has large bandwidth requirements for the amount of data that needs to be analyzed and aggregated for immediate feedback back to the running machine process
(c) The controller appliance needs to aggregate data locally during the processing of a machine job 
because the higher level systems do not need to know each little data transaction generated by the 
machine during the job. The higher level MOM functions only need summary data like job start, job end, quantity complete and quantity scrapped. This approach minimizes the requirements on bandwidth due to unnecessary levels of details traveling into the cloud systems.

The requirement for real-time control of machine processes is a very good reason for a local controller appliance. If connectivity is lost between the MOM user and the MOM application for a few minutes, it might be a bit frustrating for the user, but it will not cause a machine process to fail causing loss of invested labor and material for in-process product. The controller appliance would have a cache memory to store a full machine program before it starts a machine run or similar to how video streaming is handled for movies on demand. If connectivity is lost between the MOM application and the controller appliance for a few minutes, the appliance should be able to continue and start transmitting again when the connection is regained.

For example, in industries where the same product is made in large quantities every day for a long
period of time and only summarized information is required at the MOM level, it would be easy to see the MOM functionality hosted in a cloud service. If we only need to know at the MOM level that the machines produced 100,000 units in the current run, it would be wasted network bandwidth and application processing to send 100,000 transactions to the MOM level.

The appliance approach also provides a way to support older machines and controllers in the new Smart Manufacturing systems landscape as newer smarter machines are introduced to the factories. The Smart Machine Controller Appliance would be able to talk “smart” up to the central MOM layer using standards like OAGIS, MIMOSA, ISA88, and ISA95, and talk down to the older machines using protocol standards like OPC UA, MTConnect, EthernetIP CIP, Profinet, Modbus, MQ Telemetry Transport (MQTT).

The distributed layered model also extends into higher systems levels that operate between global
locations, into the supply chain, or into customer systems. There can be a layer of systems on top of
the MOM layer that only needs data aggregated at higher levels. At higher levels of the organizations
and business processes, we might only need work order, or project, or daily numbers. We might not need to know the details of each machine job when we are looking at performance metrics for the
organization.

Smarter machines are coming

As the standards evolve for the IIoT, newer “smarter” machines will enter the factories with the
capabilities of the Smart Machine Controller Appliance already built in. The detailed machine
transactions will be gathered in real-time from intelligent sensors and other monitoring technology
that will provide current machine conditions, the state of the production process in the work cell,
state of automated material handling, and local analysis of data for SPC control, alerts, and
programmed automated self-adjustments. Smart machines might also be equipped with sensors that verify product quality during the production run instead of waiting until the entire run is complete to do manual sampled inspection.

When machines become smarter with on-board controllers and diagnostics, the traditional functions of the SCADA system are handled by a combination of functions at the smart machine and at the MOM layer. For example, a MOM function can provide a view of the manufacturing process status or manufacturing assets’ status on a plant layout diagram.

Apps become smaller and focused

Today’s enterprise systems landscape is full of monolithic software applications that have been loosely interconnected at the database level, operate inconsistently, and are not optimized for any one function. If interoperability standards are embraced for integration of future smart manufacturing systems, not only inside the plant, but among global locations and the supply chain, I can see a future where smaller specialized apps (short for applications) are easy to incorporate into the manufacturing systems landscape. Smaller apps would be optimized for specific functions and specific users and specific hardware platforms (phone, tablet, work station or wall mounted monitor).

References:

• “Lessons Learned From Cloud in Manufacturing Industries”, Hagemeyer, Koslowski, Halpern, Scheibenreif, and Shanler, Gartner, 2014
• “The Evolution of Manufacturing Software Platofrms: Past, Present, Future”, Mark Davidson, LNS Research, 2013
• “The White Book of Cloud Adoption”, Ian Mitchell, Stephen Isherwood, and Marc Silvester, Fujitsu, 2011

 

 

Your Next Enterprise App? Survey Says: Manufacturing Operations Management

A few weeks before Gartner Research and MESA.org published their annual MES/MOM survey results [1], LNS Research released some survey results from their report, “The Global State of Manufacturing Operations Management (MOM) Software,” [2] with consistent positive trends for the adoption of MOM systems in manufacturing.

#1 - MOM is an Enterprise Level System

Why have MOM systems moved up to enterprise level importance? Manufacturers have realized that manufacturing processes are important after all. It is not enough to engineer a product and rely on outsourcing manufacturing processes. The brand owner becomes too dependent on the contract manufacturers and suppliers can become competitors after a few years of experience in the market. There is also an understanding that the old paper-based procedures of the old factory will not cut it in the new manufacturing marketplace. The path to a digital enterprise with a digital thread from engineering to production and supply chain starts with replacing the old paper-based procedures.

In the LNS survey, 68% of manufacturers are viewing MOM as part of an enterprise strategy versus 32% that see it as a plant level decision. The MESA survey also showed that 77% of manufacturers where budgeting and approving MOM systems at the enterprise level. MOM is viewed as a key bridge to realize the digital thread in the enterprise closing the gap left between engineering, shop floor, procurement and financial systems.

#2 - MOM has Real Return on Investment

Leading companies are sharing their MOM success stories in surveys, at conferences and press articles, and the word is getting out. The LNS report shows that implementers of MOM/MES systems have improved Total Cost Per unit by 22.5%, Net Profit Margins by 19.4%, and On-Time Delivery by 22%. Improvements for MOM users were generally double the average among all respondents.

Areas of improvement include:

• Common user interface across the enterprise
• Elimination of duplication of input
• Integrated interdepartmental information
• Platform of interdepartmental collaboration and workflow
• Closed-loop quality management

#3 MOM has Fast Return on Investment

MOM projects are achieving quick returns. Return on Investment (ROI) is achieved in less than 1 year by 47%, and less than 2 years by 81% of respondents. These numbers are consistent with the number of respondents finding more out-of-the-box (OOB) MOM solutions that fit their needs. Over 30% of manufacturers were able to support 80% of their requirements with OOB functions in their selected MOM solutions. This is consistent with the findings in the MESA report which showed a big decrease in the amount of customization performed in MOM software implementations.

#4 - MOM Adoption is Driven by Competitive Pressures 

Perhaps because the surveys asked slightly different questions, it is interesting that the top drivers and challenges listed by LNS are not the same as those reported by MESA. In the MESA survey the top three benefits from MOM were: increasing product quality, reducing operational cost, and improving operations visibility. In the LNS survey the top drivers included: increased number and complexity of new products, lack of collaboration across departments, inconsistent data from disparate systems, and lack of coordination across the supply chain.

A general theme among all the listed drivers is that competitive pressures are making manufacturers take notice that systems like MOM are needed to complete the digital thread in the enterprise. The capabilities gap between the Have-MES and the Have-No-MES companies will continue to grow. Companies that adopt and integrate an MES/MOM system will have a significant competitive advantage moving forward.

For more information on the LNS Research or MESA/Gartner reports check out the reference below.

References:

[1] “Four Good Trends for Manufacturing Execution System Adoption”, manufacturing-operations-management.com, 2015

http://www.manufacturing-operations-management.com/manufacturing/2015/01/four-good-trends-for-manufacturing-execution-system-adoption.html

[2] “The Global State of Manufacturing Operations Management Software - Weaving the Digital Thread Across Industrial Value Chains”, LNS Research, 2015

http://blog.lnsresearch.com/blog/bid/202211/Weaving-the-Digital-Thread-Across-Industrial-Value-Chains-INFOGRAPHIC

[3] “Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014

https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Four Good Trends for Manufacturing Execution System Adoption

The Gartner research group and MESA (Manufacturing Enterprise Systems Association) recently completed their annual survey of manufacturers (over 100) on their usage of manufacturing systems. Thanks to several years of data, they are able to look at trends in the manufacturing industry and it was generally good news in 2014 for the Manufacturing Execution System (MES) market—for both users and vendors.

#1. MES is finally Accepted as part of Enterprise IT Landscape

MES projects have a proven track record of benefits and are now viewed at the same strategic and enterprise level of importance than PLM and ERP projects. Most companies are not putting MES projects in queue behind PLM and ERP projects anymore. Manufacturing competency is recognized as an important competitive differentiator.

#2. MES Benefits Expectations focused on Improving Product Quality

More companies view quality as an integral part of their manufacturing systems strategy and are seeing the benefits of integrating quality versus keeping quality as a separate process and system.

#3. MES Barriers Move from Credibility to Agreement

As MES is now viewed as critical part of the enterprise systems platform, the entire enterprise needs to participate in the requirements definition, selection process and rollout plan. The shift from local to corporate level project can slow down projects and the achievement of early benefits.

#4. MES Implementations Move Away from Heavy Customization

The increased availability of industry specific mature MES solutions means that companies are looking at more out-of-the-box implementations and should expect less need for expensive custom extensions to commercial products. To benefit from OOB functionality, it is important that companies do their homework and look for MES products that fit their needs instead of assuming that customization is a required part of a project with any MES product. In today’s market, heavy customization is usually a symptom of selecting a product that does not fit the industry or manufacturing type.

There are many more valuable insights in the recorded webcast than the four listed above, so if you are interested in these subjects, I recommend the whole webcast and presentation which is found for members at MESA.org (see References below). I also recommend joining MESA.org for full access to this and other valuable reference resources.

Reference:

“Has MES Come of Age?”, Recorded webcast by Julie Fraser of IYNO and Rick Franzosa of Gartner, MESA.org, 2014   https://services.mesa.org/ResourceLibrary/ShowResource/47eff093-b4f9-417e-9aa1-1ddbf3e82757

 

Eight Potential Barriers to Strategic Manufacturing Process Improvements

The tune at leading companies has changed from cost cutting to product and service innovation. The need to incorporate technology innovation faster into product lines is fueling an executive push for higher levels of process improvements. Can we scale our old practices to handle higher levels of product and process change? Probably not. We probably need to rethink processes at a strategic level to enable new business paradigms and increase speed for product updates and new product
introduction.

Engineers are using the latest CAD technology to make better 3D models faster with more reusable components, more product and manufacturing information (PMI). Model-Based Manufacturing practices are leveraging 3D models to simulate and accelerate production process innovation, develop shop floor work instructions, and create automated inspection and test procedures. So what is holding back our organizations from these types of process innovations? Why are some organizations still working with 2D drawings, spreadsheets, and paper work instructions at the shop floor?

Is IT investment cost holding us back?

U.S. aerospace and defense companies have been generating large amounts of cash reserves for quite some time. The largest aerospace and defense contractors have tens of billions of dollars in cash available to them. [2] There is still uncertainty that the U.S. will pass a defense budget in the
near future but companies have to move forward with process innovation or be left behind by the competitors that are gearing up with strategic investments. The cash reserves are there in many companies to make the required investments. However, the tight budgeting processes, that organizations put in place to control the wallet over the last few years, could be holding the organization back from large strategic investments.

Is commercial software maturity holding us back?

Based on Gartner’s 2014 Hype Cycle for Discrete Manufacturing and PLM information technology [3], commercial applications for Manufacturing Execution Systems (MES) and Enterprise Quality Management (EQMS) are in the “slope of enlightenment” moving to a stable maturity state. Applications for Model-Based Manufacturing (MBM), Supplier Quality Management, Manufacturing Integration Standards, and Operations Intelligence are beyond the hype peak for early adopters and are expected to fully mature within 5 to 10 years in the market.

Even companies that are industry followers versus leaders and very conservative in technology adoption, should probably be investing in these technologies which are ready for the pragmatic majority of the market. Some technologies like Model-based Manufacturing can be implemented in a pilot area to gear up organizations for the next program or product initiative.

Are we complacent with small continuous improvements?

Organizations with a proven record of continuous process improvement through localized short term Kaizen and Six Sigma efforts might have a hard time tackling the bigger strategic projects. “Good enough” can be the enemy of excellence.

Kaizen and Six Sigma efforts look to identify and eliminate areas of waste and variability. But
focusing solely on those efforts can lead to organizations overlooking initiatives like Model-Based
Manufacturing that can enable new business paradigms and revenue opportunities. The organization can fall behind in the changing competitive landscape.

It is not always possible to achieve higher levels of performance through small incremental
localized efforts—efforts that slightly improve on old business processes. That is why some
organizations follow the Lean practice of an annual Hoshin Kanri (business strategy improvement)
effort in which the organization makes an assessment of what resources and capabilities it needs to
stay competitive, win new business and grow.

What else could be holding us back?

Significant strategic process re-engineering can only happen when organizations face head on the
following potential barriers.

Dependence on Manual Processes

With the technology available today, the organization should not be relying on paper-based or spreadsheet-based processes at the shop floor to collect data, track progress, and prepare product or service documents for the customer. Yet, a recent survey [4] by IYNO Advisors found that over 50% of the companies surveyed were depending on homegrown or spreadsheet systems for their production systems. Many other companies are still depending today on paper-based processes at the shop floor.

Even traditional Lean Manufacturing practitioners like Toyota are now embracing information technology to raise the organization’s performance to a new level. This was the topic of a presentation by Toyota US CIO, Tim Platt, who spoke about "Lean's High Tech Makeover" [1] at the 2014 Industry Week conference. If an organization like Toyota can move beyond the manual Lean processes they promoted for years, your organization can change too and embrace automated Lean processes supported by information technology.

Poorly Defined Business Processes

It can be difficult to see the opportunities for improvement right in front of us if we don’t have a
good understanding of the current business processes. I have been lucky to participate in very eye
opening value stream mapping activities with experts from production, quality and engineering
departments. I have seen how different departments can often be so focused on their own daily duties that they do not give much thought to how their work is affecting downstream activities. We expect someone else in the organization is looking at the performance of the entire system. But the reality is that many organizations do not dedicate a team to enterprise level improvement and do not spend enough time cross training personnel on business processes that cross departmental walls. Many times enterprise level processes are not documented and often very few employees understand the overall value chain across the organization. These type of value mapping exercises across departments can uncover hidden gold.

If we don’t have a good understanding of key business processes across the organization documented, it is difficult to perform a proper evaluation and optimization of the entire value stream. Tools like value stream maps, business process activity diagrams, BPMN diagrams or SIPOC diagrams for each department are very useful tools in identifying processes that might not scale to handle a lot of innovation volume and speed in our product lines.

Insufficient Accurate Information 

To justify bigger investments in process, product or equipment changes we need good data backing up the justification. We need better data to justify a new MES system which we need in order to collect accurate data to make better informed decisions in the future. Are we are caught in a Catch-22 situation?

It is important to know that we are delivering on time and paying the bills, but we also need to know more details about engineering and production processes if we want to improve those processes. It is important to know our delivered product quality, inspection results and defect counts but also more details about defect and cause classification to help us tackle the root cause of the problems.

Without the detail data, it is easy to always end up finding someone to blame for the problem
instead of taking a more methodical view to problem solving and finding the true system weaknesses. If we find that we keep blaming all problems on (a) lack of training, (b) personnel carelessness, and (c) human error, we should pause and figure out if we are jumping to conclusions because we do not have good data to identify other sources of potential issues. We want data that will point to any of the potential resources causing the problem which could be personnel related but could also be related to equipment, processes or product design.

Information maintained manually in different departmental databases or spreadsheets (information silos) is likely not consistent and could possibly lead to different conclusions and priorities in different departments. This information inconsistency can be tackled by implementing MES/EQMS information systems and integration standards that keep information synchronized between these production systems and other enterprise systems including PLM and ERP.

Lack of Flexibility to Change Processes

Are we evaluating software solutions based on our organization’s needs for growth and improvement or based on “fit” to our old legacy processes? It would be natural for the IT department to have a bias towards maintaining and improving the old custom systems that they developed versus looking to adopt new commercial software solutions. Commercial software benefits from the input and investment of many customers into one solution. If instead of looking at commercial solutions, the team keeps developing custom IT solutions and justifying the ongoing investment because the company is “different” than the competition, it might be good to get a second opinion from a neutral third party consultant.

It is also natural for engineering, manufacturing engineering, production control and quality
departments to be comfortable with the processes they have used for decades. It is good to expose
the team periodically to new ideas through trade conferences where they can see how other companies are innovating their business processes.

The cost of rebuilding custom integrations for new systems can also be a constraint. Integration
interfaces between enterprise applications based on integration standards like OAGIS make it easier
to replace functional modules in the enterprise system and stay up-to-date with best-in-class
functionality for different departments.

Fear of Too Much Change At One Time

Organizations might have a concern with making big changes all at once that will end up adversely
affecting production cost and schedule for even a few months. The risk associated with this learning
curve on new technology and business processes can be mitigated by implementing first in a pilot
area or “teaching plant” that can serve as an education platform for the rest of the organization
and proof point on the benefits and gains of implementing the new business processes.

Summary

The reality for most organizations include a mix of several of the barriers listed above. It is
important not to get put off by these challenges. Focus on how investments in new information
technology and new business processes can contribute to more contract wins, margin gains, and the
development of new agile, flexible and lean production operations. Expect production operations
remodeled based on the insights gained in your pilot implementation programs.

References:

[1] “Toyota Talks about Lean Manufacturing’s High-Tech Makeover”, manufacturing-operations-
management.com, 2014
http://www.manufacturing-operations-management.com/manufacturing/2014/05/toyota-talks-about-lean-manufacturings-high-tech-makeover.html

[2] “Aerospace and Defense Industry: Navigating a Global Future”, Trade & Industry Development, Jim Kemp, 2014
http://www.tradeandindustrydev.com/industry/aerospace-defense/aerospace-and-defense-industry-navigating-global-f-9187

[3] “Hype Cycle for Discrete Manufacturing and PLM, 2014”, Gartner, Halpern/Jacobson/Suleski, 2014
https://www.gartner.com/doc/2808322/hype-cycle-discrete-manufacturing-plm

[4] “Profitable Projects Webinar: Transforming Project-based Operations”, IYNO and Deltek,
Fraser/Kestel/Leiva/White, 2014
http://more.deltek.com/LP=3958?sourceid=49&utm_source=deltek-com-generic&utm_medium=deltek- com&utm_campaign=generic-Deltek-com&cmp=deltek-com_deltek-com-generic_generic-Deltek-com

 

Opportunities Ahead for the Internet of Things in Manufacturing

I just read a very nice Gartner paper by Simon Jacobson titled "Four Best Practices to Manage the Strategic Vision for the Internet of Things (IoT) in  Manufacturing". Good paper. 

There is a potential for convergence of IT (Information Technology) and OT (Operations Technology) in the near horizon as the standards-based IT technologies make their way into the new generation of manufacturing machines and systems. Some of the forces helping this convergence include the growing needs for interorganizational collaboration, a desire to mine more realtime data, the expansion of mobile and wireless technologies, and trends to embrace more bring-your-own-device policies in our plants.

The IoT doesn't have to be on the Internet.
The IoT doesn't have to be on the Internet.

I repeat that twice because I have heard several skeptics discounting the technology opportunity because they don't believe that the plant equipment will ever get direct access to the Internet. However, I am hoping the term used by Simon, "The Intranet of Things", catches on. In the short term, we should focus more on the converge of IT and OT, and less on trying to push data into the
cloud.

Some of the examples of the manufacturing IoT opportunities include:
* Automated kanban replenishment across lines/sites/tiers
* Job prioritization via machine learning and sensors
* Remote monitoring and alerts via personal mobile devices
* Accelerated quality checks via asset connectivity
* Automated equipment configuration based on product RFID recognition

These are just some teaser points from the paper. If you have access to Gartner's manufacturing reports, the full paper is a must read. 

Reference:
Four Best Practices to Manage the Strategic Vision for the Internet of Things in Manufacturing
https://www.gartner.com/doc/2899318

Smart Manufacturing needs a Real-time Integrated Enterprise

The smart manufacturing factory employs 21st century technology to fabricate and assemble a mix of configurable products in response to immediate customer demands. It implements advanced processes that can incorporate technology innovation as fast as scientists and engineers develop it. It marries information, technology and human ingenuity to bring about a rapid revolution in product design, development, manufacturing, distribution, sales and service. It delivers these process improvements while also improving worker safety, minimizing environmental
impact, and improving product quality.

The smart manufacturing factory is at the intersection of a few technology initiatives including the development of smart manufacturing devices that are part of “The Internet of Things” (IoT), 3D model based manufacturing, and a re-architecting of enterprise systems around services and integration standards to improve communication speed and accuracy not only within the organization, but also with the entire partner and supplier network.

Smart Manufacturing Automation Devices

Manufacturing machines are getting smarter and are equipped with their own computers that control and coordinate a myriad of microprocessor chips on every sensor, motor, and actuator. Robots have been working behind fences automating many of the dirty, dangerous and dull work in the factory, and work requiring high levels of precision and consistent repetition. A new generation of easily configured and set up robots are being developed to assist human personnel side by side. For example, a prototype robot named Baxter (by Rethink Robotics, http://www.rethinkrobotics.com/baxter/) is taught his task through a demonstration by the operator on how to move its arm and hands to accomplish the task.

In the past, each advanced manufacturing center was managed as a process island within the plant operated by a few experts with its own special procedures and controls. The new generation of smart manufacturing equipment connects to the wireless network as easily as our cell phones and
computerized glasses (wearable technology in general), and will be ready to receive input and broadcast feedback. This creates an opportunity to standardize on new equipment and application data exchanges—machine-2-machine (M2M) and machine-2-application (M2A) —that eliminate the information silos within the plant.

For example, our espresso machines can already broadcast when they need coffee beans and when they need to be cleaned via a display at the machine. What if the machine also broadcasted that information to our home computer? An application on our computer could be tracking the inventory level of coffee in the pantry, the weekly usage pattern from our coffee machine, and could use this information to forecast when we would run out of coffee and even automatically put coffee on our shopping list. These are some of the promised concepts at home from the “Internet of Things” (IoT) (http://www.iot-a.eu/).

IoT concepts extend to the factory floor and the next generation of smart machines. There are efforts for a Common Industrial Protocol (CIP) to facilitate these type of data exchanges in the factory between machines, computers and personal devices.

The German government refers to this next generation of connected manufacturing machines, robots and information systems as “Industry 4.0” (http://www.bmbf.de/en/19955.php) and looks at it as the fourth industrial revolution. But to make Industry 4.0 a reality, equipment manufacturers will need to embed intelligence and communication capabilities into their products and we will need broadly accepted standards for communicating, collecting data and interacting with the equipment.

In the United States, NIST’s Smart Manufacturing Operations Planning and Control Program (http://www.nist.gov/el/msid/syseng/smopc.cfm) aims to enhance U.S. innovation and industrial competitiveness by facilitating the adoption of smart manufacturing systems (fully-integrated, collaborative manufacturing systems that respond in real time to meet changing demands and conditions in the factory, in the supply network, and in customer needs). This program will enable smart manufacturing based on efficient networked sensing and control, prognostics and health management (including diagnostics and maintenance), integrated wireless platforms, industrial control security, efficient information exchange and interoperability of system components.

Real-time Integrated Processes and Information Systems

IoT deployments will generate large quantities of data that need to be processed and analyzed as close to real time as possible. If a system can’t keep up with the volume of data, it will increasingly fall behind and its analysis and processing of old data will quickly be obsolete and of no value.

The recent trends in enterprise information technology (IT) have been to centralize applications to reduce costs. However, organizations will need to rethink that IT strategy or risk creating a bottleneck in an IoT world. The processing of data streams from smart devices will need to be
distributed and layers of systems will aggregate and filter the data from lower level systems close to machines, to layers of systems managing business processes (internally and into the supply chain) and all the way up to enterprise systems with centralized financial information for the business.

This might sound similar to having a SCADA or historian system collect machine information and pass filtered and aggregate data up to a Manufacturing Operations Management (MOM) or ERP system, but it is different because the smart machines can do much more processing and
filtering of the data themselves and will be communicating with the same protocols that our cell phones and TVs use. For these smarter devices, we can probably skip the traditional layers 0-2 in the old ISA95 and ISA88 models. The machines might broadcast straight to a cloud service which in turn can broadcast to our personal device which has been registered via the company’s portal to receive specific types of messages.

From the product design side, especially in Aerospace and Defense industry, the “Model-Based Enterprise” (MBE) (http://model-based-enterprise.org/) is redefining how manufacturers work with engineering and suppliers in the digital 3D world. Product definitions in 3D formats need to be communicated through the supply chain and through our own internal systems for procurement, production and quality control. Change management practices in the 3D world set the bar higher than what could ever be achieved via 2D drawings and paper forms. The goal is full associativity between engineering, inspection, manufacturing, and service definitions to facilitate change and configuration management practices among multiple tiers in the supply chain.

NIST has a digital thread for Smart Manufacturing that picks up the work from MBE and takes it downstream and links it to the Smart Manufacturing infrastructure (http://www.manufacturing.gov/docs/DMDI_overview.pdf)

Over 50% of the actual manufacturing of a product happens in the supply chain and over 50% of the data exchanged with partners and suppliers still travels today over email, phone and fax rather than flowing directly between business applications via B2B (business-2-business) integration in
structured XML. This low adoption level of B2B integration is surprising given that the first EDI systems were introduced four decades ago and XML standards have been around for decades also. There has been a lack of a concerted effort to tackle this integration arena. Perhaps because
organizations have been plenty busy trying to orchestrate A2A (application-2-application) integrations within their four walls. However, with such a big percent of the success of the company riding on the reliability of these communications, it is time to start improving B2B
integration techniques with our supplier network. 

MESA (http://www.mesa.org) and the Open Applications Group (OAGi, http://www.oagi.org/) have recently signed a collaboration agreement. OAGi is a standards organization managing XML integration standards for A2A and B2B communications and is widely used in the discrete manufacturing arena.

APICS has recently joined with the Supply Chain Council (https://www.gartner.com/doc/2728128/supply-chain-councilapics-merger- help) and they have the SCOR framework for supply chain management.

The SCOR model addresses business practices but do not translate to B2B integration requirements. OAGi is working on mapping SCOR to B2B integration standards. NIST is also working with OAGi on integration standards that span into the supply chain.

Manufacturing organizations that want to lead the next generation of supply chain communications should join these organizations and participate in these efforts.

Benefits of a new real-time integrated enterprise platform for smart manufacturing include:
• Accelerate time to market for innovative products
• Future-proof the IT investment with flexibility to easily switch best-in-class functional modules
• Reduce total cost of ownership with simplified management across the platform by leveraging plug and play integration
• Ensure tight integration between enterprise applications and operational systems at the shop floor
• Increase speed and accuracy throughout the extended value chain
• Gain insight you can act on with metrics acquired directly from transacting the business process model

 

 

Are We Keeping Our Machines Happy?

I love this story. "What happens if Voyager runs into a planet? Who will fix it if it breaks down?"A five-year-old boy has questions for Astronaut Cmdr Chris Hadfield. He was worried about the Voyager alone in space.

 

 

 

Audio: http://www.cbc.ca/player/News/Canada/Audio/ID/2530849070/

Makes me wonder if we are keeping the machines at the shop floor busy and happy? What about the next generation of smart machines and robots? Will they be happy? Will we be as interested in our smart machines as the boy with Voyager?

The Collapse of the ISA95 Manufacturing Operations Management Model

Not “collapse” as in demise, but as in collapsing the levels used in the model. Isn’t it time to give the ISA95 model a little overhaul? The ISA95 team built a framework for modeling business processes with 4 levels of systems in an enterprise and placed Manufacturing Operations Management (MOM) applications on Level 3 of the model. Levels 1 and 2 were left for ISA88 to explain direct machine and recipe management, and Level 4 was supposed to be where “applications we don’t care about at the plant floor” go, applications like ERP. Manufacturing systems feed Level 4 applications in the background, but hopefully, at the shop floor, we do not have to look at those Level 4 apps or our eyes will burn : ) 

The concept that information has to flow up in the model before it moves out of the organization is a little outdated given that 50% of a product is usually now manufactured outside the company walls. In today’s manufacturing world, MOM systems do much more than just manage the plant, they also manage critical communications and transactions with suppliers of material, parts and specialized external processes like paint, chemical processes, testing, etc. That means that MOM systems are not just doing application-2-application (A2A) communications, they are also doing business-2-business (B2B) communications which (in theory) were supposed to be the job of Layer 4. These communications might need some coordination with the procurement function, but do not necessarily need to go through it.

When I take a business process management (BPM) view of manufacturing processes across multiple disciplines, I don’t really see “levels” as in ISA95. Business processes need to orchestrate communications and transactions with objects, applications and people in different disciplines and all appear to be at the same level. The level of data detail required (for example, seconds versus days) might be different for different applications or transactions, but precision requirements can be viewed as attributes of each transaction and not necessarily derived from a made up theoretical “level” for an application.

Can the “levels” in ISA95 become a constraint for manufacturing systems development and architecture if they are taken literally? If you look at the Purdue CIM model as a predecessor to the ISA95 model, the levels were closely tied to levels of management. Data would be rolling up and aggregated from lower level transactions similar to rolling up facts in a data warehouse to higher level metrics and scoring for executive management. The levels were not intended to represent walls for applications or systems. A reference model, like the Purdue model, is a good reference to have around and help us understand how functions in our own organization could be wired, but innovation can happen when we rewire the organization differently.

We are now witnessing the evolution of the Internet of Things (IoT) along with standards and protocols to facilitate machine-2-machine (M2M) and machine-2-application (M2A) communications. For manufacturing, the IoT means that industrial machines will be smarter with internal computers and internet connectivity, practically acting as any other computer, laptop or phone on the network. When a new piece of equipment is installed, it will present itself to the control system along with its operational constraints and energy profiles. The control system then can incorporate these to form a control strategy for that machine. As a result, the machine becomes part of an intelligent, machine-led optimization engine, where resource availability, product demand and energy costs can be easily viewed and weighed to provide the best production schedule. Do we still consider these smart machines and their built-in applications to be at Levels 1 or 2? Or do we see work happening at different levels within those machines?

The German government calls this next generation of manufacturing machines, robots and systems, Industry 4.0. But to make Industry 4.0 a reality, manufacturers will need to embed intelligence and communication capabilities into their products and we will need broadly accepted standards for communicating and collecting the data.

I am putting the idea out here to see if there is interest. I think it is time to start thinking of newer models for manufacturing systems that span across B2B, A2A, M2M, M2A types of communications with a BPM flavor of communications in mind facilitating implementation of Lean Manufacturing concepts, autonomous yet coordinated work cells, and aggregation of real-time manufacturing intelligence. 

Please share your thoughts on this topic and any ongoing initiatives you have that relate to this topic.

Four Lessons Learned from Early Cloud SaaS Experiences in Aerospace Manufacturing

Gartner analysts (http://www.gartner.com/) recently published a paper “Lessons Learned From Cloud in Manufacturing Industries” sharing experiences from companies in Aerospace & Defense (A&D), Medical Devices, and Automotive industries that have been piloting solutions for product lifecycle management (PLM), manufacturing operations management (MOM) or supply chain management (SCM) in the cloud or  SaaS (Software as a Service). Some of the lessons most applicable to information technology initiatives for regulated industries like A&D and Medical Devices are listed below.

Gartner analysts point out that the cloud and SaaS offerings have a lot of potential benefits in the near future as they enable new collaboration paradigms inside the company and with partners, but they warn that “cloud for cloud’s sake” approaches will probably not yield much benefit. They expect cloud technology to further mature over the next two years and conquer some of the current constraints.

Four of the lessons learned and risks identified in their research:

#1 – Higher Costs
Many users of cloud solutions expect reduced costs, but do not necessarily get it. Cloud strategies can end up costing more than in-house solutions. For example, applications that perform a lot of
calculations can end up increasing infrastructure as a service (IaaS) costs—especially during high demand periods.

#2 – Integration and Workflow Constraints
Many multitenant applications offer one-size-fits-all integration and workflow options that might not suffice for your organization. Integration interfaces and workflow rules required between
enterprise applications (including CAD, PLM, MOM, QA, ERP, and SCM) to support your organization’s business processes might not be supported by the current crop of cloud offerings.
Just because an application is moved to the cloud does not guarantee that collaboration options are automatically improved. Applications have to be redesigned to take advantage of new collaboration paradigms enabled by the cloud.

#3 – Security Concerns
Most security risks with cloud-based solutions are overhyped. There were no reported breaches among the surveyed companies and the data is secured behind firewalls as it would be with an in-house server strategy.

However, current cloud solutions (a) lack ITAR (International Traffic in Arms Regulations) support inhibiting use by many A&D manufacturers, and (b) require 25%-50% higher implementation costs to meet GxP compliance requirements for additional logging, auditing, archiving, and nonrepudiation controls.

Some manufacturers have expressed a desire to keep data assets behind their own firewalls and use the cloud as a collaboration platform. This requires re-architecture of business processes and
applications.

#4 – No Quicker Startup Time
Even though the application is quickly available and users can quickly be added, the implementation of a new cloud solution still needs upfront planning and many of the same steps required in any implementation project. Implementation projects, regardless of cloud or on-premise architecture, require the same level of project management to be successful. Organizations receive the most benefits when they can re-engineering business processes to take advantage of a new solution, and these efforts will take considerable time and resources.

If you have access to Gartner reports, I encourage you to download and read the full report.

References:
“Lessons Learned From Cloud in Manufacturing Industries”, Hagemeyer, Koslowski, Halpern, Scheibenreif, and Shanler, Gartner, 2014

 

Managing Risk and Improvement in the Extended Enterprise via Corrective Action

Both continuous improvement and risk management are considered minimum compliance process requirements in the latest versions of Quality Management System (QMS) regulatory standards including ISO9001, AS9100, and ISO13485. Corrective and Preventive Action (CAPA) projects can be used to manage both risk mitigation and process improvement types of projects. 

When we look at processes in the entire product lifecycle starting at the supplier, continuing through manufacturing into aftermarket maintenance and repair services on the product, we find the following sources for potential CAPA projects:
• Audit Findings
• Inspection Findings
• Discrepancy Reports
• Equipment Maintenance Trouble Calls
• Product Warranty Claims
• Customer Complaints
• Performance Metrics Analysis

Whether a project is needed to mitigate risk or improve performance, many of the steps we follow are common and merit a similar discipline. Lean Manufacturing and Six Sigma practitioners use slightly different terminology and methodologies for process improvement, but they are really more similar than different.

Six Sigma practitioners use the DMAIC process to systematically reduce variability in a manufacturing process. Lean practitioners refer to the Toyota problem solving methodology and to kaizen events.

For diving down into specific urgent issues once they are uncovered, the Toyota Problem Solving process and the 8-Discipline (8D) process are very similar. The 8-Disciplines (8D) process is often used because it adds steps to contain the problem.

The Toyota Problem-Solving process and the 8D process have a lot of similarities but in many ways do not exactly align. The 8D process has an emphasis on corrective and preventive actions which are more appropriate to obvious quality issues, and the Toyota Problem-Solving process has more emphasis on defining the problem which might not be obvious on productivity issues.

Form the Team – The 8D process recognizes the importance of assembling the right team and places it as the first step. A small team is needed with the right mix of skills, experience and authority to resolve the problem and implement solutions. Ensure that everyone on the team has the time and inclination to work on the problem together. Formal meetings should initiate the effort and monitor progress. The company’s Corrective Action Database should be used to document findings, decisions and progress.

Describe the Problem – It is critical to define a clear problem. The problem statement provides the starting point for investigation. Terms used in the description must be understood by the entire team. Problem description should include the following:
(a) Who – Who is affected by the problem? Who first observed the problem? To whom was the problem reported to?
(b) What – What type of problem? What has the problem? What is or isn’t happening? What is the physical evidence?
(c) When – When did it first happen? When was it first observed? Is it a recurring problem? How often? What is the trend? (random, cyclical, continuous) Has it been observed before?
(d) How Much – What is the magnitude of the problem? How many units or components are affected? How much cost (labor and material) is associated to this problem?
(e) Why – Why is it a problem? What is the impact? What processes are affected? Is production stopped completely or partially? Is there a work-around? Are customer delivery dates impacted?

Some of the questions above might also be viewed as assessing the risk and impact of a potential problem recurrence. The risk can be given a risk score by multiplying a Likelihood Rating, Severity Rating and a Detectability Difficulty Rating.

Contain the Problem – Identify interim containment actions to prevent further cost impact from the problem. Is the problem tied to a machine? Is it tied to a supplier component? Are more lots affected by this problem? Suppliers must be informed as soon as possible about issues to prevent further shipments with problems. Inventory parts which might be affected must be identified and inspected. If problem has a life- threatening consequence, products in the field have to be identified for potential recall.

Identify the Root cause – An investigation and failure analysis is performed to determine the root cause of the problem. It is not enough to identify the first-level or intermediate-level causes of the problem. The true root cause has to be uncovered and corrected or a similar problem will eventually manifest itself.

Root cause analysis may start with brainstorming sessions among team members and move on to data analysis that further explores multiple potential causes. Different kinds of charts and diagrams are used in data analysis including histograms, Pareto charts, scatter charts, and concentration and affinity diagrams.

It is important to document the analysis discussion, findings and conclusions using cause-and-effect charts and fault tree diagrams. A five-whys type of analysis is also useful to understand all the intermediate-level causes between the root cause and the problem manifestation.

Corrective Actions – This discipline entails identifying all possible corrective actions to address the root cause of the problem. The owners of the corrective actions, the target dates for completion and the rationale behind each should be documented. It is sometimes a good idea to have a preliminary evaluation of the corrective action to test its effectiveness before wasting time and money on fully implementing an incorrect solution.

Corrective action includes further containment of potentially affected similar production or inventory based on the root cause identified. If problem has a life-threatening consequence, products in the field have to be identified and notifications issued for recall or repair.

Permanent Correction – Once the corrective actions have been defined and tested, the next step it to implement them and verify their effectiveness. If the corrective actions are found to have deficient effectiveness it might be necessary to return to the prior step and create new corrective actions.

Preventive Actions – Lessons learned from the above investigation and corrective actions might be able to benefit other processes or areas with similar vulnerabilities even if not affected under the current situation. Preventive measures should be outlined if it is possible to prevent similar problems in the future. Preventive actions, like corrective actions, require owners and target dates.

Acknowledge Success – The last step of the 8D process is management’s formal recognition of the accomplishments of the team. This demonstrates the commitment of the management team to the continuous improvement process and is good internal publicity for the effectiveness of these initiatives.

 

Unique Requirements for Operations Management in Complex Discrete Manufacturing

What is Complex Discrete Manufacturing?

Complex discrete manufacturing industries manufacture complex highly engineered products with longer product cycle times and multiple levels of subassemblies in their bills of material. Many of these companies make and engineer products to order. They make “discrete” product units, as in individually separate or distinct, and they need to track manufacturing history down to each serialized product unit. In contrast, batch production manufacturers track production per work center, day, batch or lot.

Complex discrete manufacturers include companies that manufacture products for aircraft, space, military weapons, complex medical devices, robots, and specialized industrial equipment. For many of these products it is necessary to manage complex diverse product configuration, long product life cycle, along with increased market pressure for shorter time to market for new products and stricter regulatory compliance oversight.

Some of the characteristics that define this type of manufacturing environment include the following:

• Long cycle times, low volume, make-to-order or engineer-to-order
• Complex product with deep bills of material (BOM)
• Highly skilled labor performing manual assembly and fabrication work including complex NC machines and special materials like composites
• Complex process routing sequences with decision points and loops
• High flow of engineering changes affecting work-in-process
• Production is not repetitive and mechanics must be alerted to changes
• Data collection during production includes manual data entry, verifications and signatures
• Personnel have qualification requirements and equipment have calibration and certification requirements
• Documentation requirements include complete history for each product unit and traceability of components installed and material used

Unique Requirements for Manufacturing Operations Management

On the surface, many manufacturing systems seem to have a similar functional footprint, but some cater to specific industries with very specialized functionality. To make an informed assessment of a manufacturing operations management (MOM) system it is necessary to drill down to more specific requirements and narrow down to the solutions that can truly handle your industry and type of manufacturing. Finding the right solution fit can hugely affect the effort, time frame, cost (total cost of ownership) and results of a MOM system implementation initiative.

Complex discrete manufacturing organizations have unique requirements in their manufacturing and quality management business processes including: (a) carefule resources certification management, (b) complex product/process configuration and change management, (c) detailed integrated quality control processes, (d) detailed product unit history and records archival.

Watchful Resources Certification Management

Personnel must be certified competent on the basis of education, training, skills and experience. Personnel qualification processes must be standardized and documented. In addition, equipment resources must also be maintained to assure their capabilities, especially measurement equipment used to verify the product. The equipment and tools maintenance and calibration processes must be standardized and documented. An MOM system can verify calibration status for equipment and also verify that personnel signing a job has the required active certifications.

Complex Product/Process Configuration and Change Management

The manufacturing of a complex product like an aircraft or satellite involves the management of a continuous stream of engineering changes directed at work in process. The integration of engineering systems and MES can create a seamless link between product development, manufacturing planning, and manufacturing execution functions that closes the loop on engineering changes and assures that as-built configuration matches as-designed.

Detailed Quality Control Processes

Beyond providing visibility into areas for improvements, the manufacturing information system should provide process control procedures to implement and sustain quality improvements including in-process inspection and verification steps, statistical process control (SPC), alerts to out-of-control conditions, and integrated handling for discrepancies found during production including defect containment and corrective actions to eliminate recurrence.

Because of the high investment in parts and labor that goes into these types of products, they are rarely scrapped. Instead, these industries require rework, repair and deviation handling procedures to ensure that deviations are documented, reviewed and approved by the appropriate personnel. The integration of production and quality systems can ensure that deviation instructions cannot be skipped by the mechanic performing the work. Deviation history is also considered part of each product unit history.

Detailed Product Unit History and Records Archival

The manufacturing information system needs to maintain production history documentation down to the details in each product unit versus tracking to the batch level. The system documents exactly who, what, when, how and why―who completed the job, what equipment was used, which parts were replaced, and who approved the changes.

 

The Dangers of OEE as KPI for Manufacturing Operations Management

There is much hype in some circles about OEE (Overall Equipment Effectiveness) as a KPI (Key Performance Indicator)  for manufacturing companies, and as we know from experience,  we must always approach hype with some level of skepticism. This article discusses three reasons why OEE is a potentially dangerous KPI for a manufacturing company to rely on for business decisions and operations management.

What is OEE?

OEE is a measure of how well equipment in a plant are utilized in relation to their full potential. It was conceived to quickly identify areas contributing to big productivity losses including breakdowns, long setups, frequent stops, and defective output.

OEE = Availability x Performance x Quality. Availability, performance, and quality are good solid metrics, and the OEE formula is simple, so why not use OEE as a KPI for the organization? What dangers could be lurking under such simplicity?

Danger #1: OEE does not relate to the company’s true business goals

If the company gets paid to run machines at full capacity all day, OEE might be a decent metric for the business. For example, if you run an electric power company or a chemical processing plant, you might find that OEE relates to your bottom line. However, that is not the reality for many manufacturers. Companies that manufacture discrete products to fulfill customer orders have business goals related to factors that influence customer buying decisions: Schedule, Price/Cost, and Quality. Their KPIs should align with their business goals and OEE does not.

Danger #2: OEE does not address the real constraints to production 

From studying Eliyahu Goldratt’s “The Goal” and his Theory of Constraints (TOC) principles, we understand that the most important considerations in manufacturing operations are to keep the plant running to a “drum beat” and to mitigate the risks of any constraints that can affect the plant rhythm and choke the production rate. The Theory of Constraints is a holistic view that takes the entire plant into account. OEE is focused on local optimization of each work center, but the goal is optimization of the entire production system. OEE assumes that the goal is to keep each work center busy and producing at 100% capacity all the time. However, in the context of the entire production system, it might be acceptable to have areas of low utilization.

The goal is not to keep every work center and piece of equipment busy all the time; the real goal is to get product out on time to match demand, at a low cost, and with high quality. The organization’s metrics should be directly related to the real business goals that lead to the ultimate goal of most manufacturers: higher profits.

The Theory of Constraints is used by manufacturers to identify their production bottlenecks and then work to improve and eventually eliminate them. Not all resources in a plant are potential bottlenecks, so only resources that are (or have potential to become) constraints to production should be closely monitored and optimized for the company to achieve its real manufacturing goals. If the organization focuses on “fixing” work centers with the lowest OEE numbers, it might be under optimizing the overall macro manufacturing process. 

Danger #3: OEE is an aggregate metric that obfuscates instead of clarify areas for improvement

Aggregate measures like OEE have the risk of hiding underlying issues. Each component of OEE in and of itself (availability, performance and quality) provides better visibility into the organization’s performance. When the sub-metrics are multiplied by each other, as is done with OEE, the resulting number can end up hiding the areas that have the most problems. For example, an area might have high availability and utilization numbers, but a low quality number, but because all of the numbers are multiplied together, the low quality number is hidden and therefore, not addressed.

Not only does OEE hide underlying issues, but it also muddies the waters when it comes to determining areas for improvement. OEE assumes that each of the sub-metrics have equal importance, but for many organizations, a 1% labor performance loss is not as important as a 1% quality loss. For example, it is easy to increase quality by increasing cost. The trick is to increase quality while reducing cost. An area with 90% quality and 70% performance has a different problem than an area with 70% quality and 90% performance, but they can both have the same OEE rate. 

In Summary

Considering the three dangers stated above, do OEE numbers really tell us anything important or useful about our business? Will they lead to more sales and profit? Or do they potentially misguide improvement prioritization efforts?

In addition to the dangers listed above, using OEE as a way to benchmark the business against others is not wise unless we are comparing across very similar types of businesses. The idea that you can have a benchmark goal for OEE of 85% across the industry is unsound. Perhaps it should be 95% for one type of process, and perhaps 70% is okay for another type of process.

If OEE is not the silver bullet metric for manufacturers, which one is? We shall explore that topic in a future blog post. What metrics should an organization look at to monitor profit, schedule and quality goals? What metrics help identify areas for improvement and areas of efficiency loss due to poor quality, low speed, equipment downtime or supplier issues? We should explore some Lean Six Sigma metrics like Cycle Time Efficiency and methods of identifying the underlying causes behind the loss of efficiency. Stay tuned. 

More information on OEE definition in this older post: 

“Overall Equipment Effectiveness (OEE) Or Overall Resource Effectiveness?”
http://www.manufacturing-operations-management.com/manufacturing/2009/11/overall-equipment- effectiveness-oee-or-overall-resource-effectiveness.html

 

Toyota talks about Lean Manufacturing and Digital Technology

I was at Industry Week’s annual conference last week and had the pleasure to listen to one of Toyota’s VP of Information Systems, Tim Platt, talk about "Lean's High Tech Makeover".  Mr. Platt shared how Toyota is leveraging information technology in their current Lean Manufacturing efforts to gain more efficiencies. He showed examples of how information technology is supporting the Lean principles of Jidoka, just-in-time and standardization.

Three case studies were presented for US plants totaling around $3.5M per year in hard savings from implementing information systems to achieve the Lean principles.

The following are some examples of how information technology is helping achieve new levels of
performance with these Lean principles:

Jidoka Lean principles include the ability to stop a process when an issue is found and
immediately alert the supervisor and support personnel that can assist in solving the problem. Old school Andon boards are replaced by visual dashboards that provide the same visibility within the work center but also provide visibility from any computer in the plant to any work center Andon board. Old school Andon lights used to indicate that the line was stopped are replaced by event triggered alerts that are broadcasted via email to the respective support department to assist in clearing the issue as soon as possible.

Standardization principles are enforced via a manufacturing execution system (MES) that provides
work instructions to encourage process repeatability and reduction of variance.

Pokayoke tooling and mechanical methods are complemented with sequence and data collection
enforcement by an MES to make sure the correct process is followed every time.

Just-in-time principles can be applied with system generated Kanban triggers. Heijunka type work
leveling to achieve smooth flow can be assisted by the use of special scheduling software that
incorporates Lean principles and similar principles like drum-buffer-rope.

It was very refreshing to hear a Toyota executive talking about leveraging information systems
to implement Lean principles after years of hearing old school Lean practitioners taking the
hard line against information technology.

Some of us have talked for years about the information flow being a critical part of the value
chain that requires waste reduction to achieve a Lean Information Value Chain.

The examples from Toyota, one of the companies that taught us Lean Thinking, continue to
reinforce that information technology and Lean Manufacturing are not mutually exclusive. Another source for examples leveraging information technology to achieve Lean is MESA’s “Lean
Manufacturing Strategic Guidebook”; a resource available to MESA premium members at mesa.org.

References:
“Toyota’s Lean High-Tech Makeover”, a presentation by Tim Platt, VP Information Systems, Toyota US, May 2014
“Lean Manufacturing Strategic Guidebook”, MESA International, 2010

Was my Smart Phone a Smart Purchase? What about my MES Purchase?

When was the last time you purchased an item for home based on return on investment (ROI)? Most of the time, we make up our minds on what we are going to buy without doing any numbers and we just go shopping based on price and value. But for an example of an ROI analysis, I will look back at the purchase of my current smartphone.

I used to carry around a regular cell phone which I loved because it was small, light and had a long battery life. So when I went to buy a new phone I was not sure about getting a smart phone. As any industrial engineer would do, I did an ROI analysis to see if it made sense.

My approx. labor time
= $100,000/year / (50weeks x 40 hours/week) = $50/hour (very rounded numbers)
My extra time working thanks to smartphone
= 10 trips/year x (2 hours usually idle at airport/trip
+ (1 hour work recovered between meetings x 3 days/trip))
= 50 hours/year
Savings /year = 50 hours/year x $50/hour = $2500/year

Investment Cost = $200 for the phone
Recurring Cost/year = $700 data plan + $100 IT support = $800/year

Payback is less than one year !

The ROI calculation would have been the smart thing to do, but who does that? Not even my IT department. The justification for smart phones is obvious and does not really need this type of analysis. The intangible benefits alone are even greater than the tangible ones listed above.

The intangible benefits of the smartphone include:
• Email anywhere. What is the cost of missing an opportunity because I didn’t get the email on time or upsetting a customer who was waiting for a reply to his urgent email.
• GPS. I will never get lost in an unknown city again. There is also a tangible cost savings here for not having to rent the GPS with my car rental.
• Web access anywhere. I can look for a nearby restaurant using Urbanspoon, or Google that trivia that came up during dinner conversation.
• Music player. I don’t have to carry around an additional device like an MP3 player anymore.
• Apps. I cannot live without the flight status application from the airline. I have used it to determine when I was going to miss connections and rebooked right from my phone. Priceless!

I have been wondering if the same buying patterns apply to some purchases at work including purchases for some information systems. Were the purchases made based on ROI? Perhaps ROI should not be the only criteria.

In the report titled “Insufficiently Defined Business Cases and Application Strategies Are Holding Back the Value of MES,” Gartner states that the lack of a clear ROI is one of the biggest obstacles for Manufacturing Execution System (MES) projects listed by manufacturing Operations management executives. However, the same month Gartner’s report titled “Extend the Value of MES Beyond the Plant” lists that manufacturers have actually reported many greater intangible significant values for MES and most of them realized after the first year of implementation. Perhaps we shouldn’t need an ROI study to get MES projects approved. The intangible values identified include (a) using data from MES as the basis for continuous improvement, (b) increased product quality, (c) standardization of processes, (d) reduction of cycle time for resolution of issues, and (e) easier new product introductions. These Gartner reports have a lot more details and are worth downloading if you have access to them via Gartner or MESA. MESA is also publishing a new ROI Guidebook for manufacturing systems in the next few weeks.

Users at companies that have successful MES implementations say that they do not want to go back to the old paper-based forms. I was at a plant that had implemented a new paperless process with MES at one building for a new program and those employees dreaded going on loan to the other building. Just like I would dread going back to my old plain cell phone. I do not want to lose all the functionality I have in my new smartphone.

Of course, if you just use your smartphone to play Angry Birds, then there is no ROI. The same applies to the MES purchase. If your company doesn’t use the MES to its full extent, then the company will not realize the full expected value until it is fully implemented.

Do we really need to do the numbers when the decision is that clear? Or do we need to make these decisions the same way we make our smart phone decisions? Did the same companies, holding up MES projects, do the ROI for their ERP systems? Or was that decision obvious to the CFO and did not require the same level of justification? With all the documented success stories for MES, the only debate companies should be having is which MES fits the business better and who should help implement the MES, not whether or not there is a need for MES in the factory.

What do you think?

 

References:

• “Insufficiently Defined Business Cases and Application Strategies Are Holding Back the Value of MES”, Jacobson and Dornan, Gartner, 2014
• “Extend the Value of MES Beyond the Plant”, S. Jacobson, Gartner, 2014

MES versus MOM Terminology Confusion – Manufacturing Execution System or Manufacturing Operations Management

It is 2014 and the MESA (www.mesa.org) organization is wrestling with an important decision. Does it keep using the term MES (Manufacturing Execution System) or move to the term MOM (Manufacturing Operations Management) to describe manufacturing systems? The debate was very active in LinkedIn’s MESA International group forum and also at a recent MESA webcast panel discussion.

Is it time to change the term “MES” to another term just when it is starting to gain recognition and acceptance? Is there any good reason for the change?

First some historical perspective

The terminology timeline shows the introduction of acronyms for manufacturing systems over the last few decades in three major areas: engineering, inventory planning, and production shop floor. We can see in the diagram that just about every 10-12 years, the software industry gets an itch to introduce a new term. These new terms usually signal an evolution in function or footprint for a software category. It has been one of the ways the software industry defines “progress”.

For example, PLM (Product Lifecycle Management), was introduced in 1995 as an evolutionary vision above the definition of PDM (Product Data Management). Software vendors working on solutions for the engineering departments soon jumped to use the term and fulfill that vision. The bar was set high for the PLM vision and vendors and manufacturing companies are still working on fulfilling that vision.

The term ERP (Enterprise Resource Planning) stopped evolving in the mid 90’s. Did that signal a maturity for that category of solutions? There were minor attempts to introduce an ERP 2.0 term around 2007 and 2010 but they didn’t succeed. The term MES (Manufacturing Execution System) has been around since the early 90’s when it was introduced by AMR Research to be the successor of the term CIM (Computer Integrated Manufacturing).

The CIM term was introduced in 1973 by Joseph Harrington and published in his book “Computer Integrated Manufacturing” in 1979. The term gained popularity around 1984. In the CIM wheel published by SME (Society of Manufacturing Engineering) we start seeing functional blocks for CAD Design, Scheduling, Tooling, Production Control, Quality Control, Maintenance, Material, Fault Diagnosis, Material, and Document management. Building blocks that we will later see in the MES definition.

After the MES term was introduced by AMR Research, the MESA organization embraced it and started writing on the subject and educating manufacturing companies on the importance of creating a platform for managing operations at the shop floor. Michael McClellan also published a book in 1997, “Applying Manufacturing Execution Systems” which further defined the term and the functionality scope.

From page 2 of McClellan’s MES book: “MES is the step of integrating all of the activities between the planning layer and the automation layer as components of a proactive integrated on-line system providing a synergistic process that is greater than the sum of the parts.”

With the definition of MES by MESA we moved from “wheels” to “gears”. Note that there are gears in MES for everything you will later find in the MOM definition and more. Also note that McClellan had introduced a layer of Production Planning on top of MES and a layer of Automation Control under MES. The MESA papers also explained that MES would integrate with ERP, Supply Chain Management and Engineering Systems.

Areas of MES include from the following from its inception:

• Resource Allocation and Status
• Operations/Detail Scheduling
• Dispatching Production Units
• Document Control
• Data Collection/Acquisition
• Labor Management
• Quality Management (real-time analysis of measurements, problem identification)
• Process Management (monitoring)
• Maintenance Management
• Product Tracking and Genealogy
• Performance Analysis

It is also important to note that McClellan’s book also referenced several industry leading companies that were doing MES. This was not just an academic wish list. Discrete manufacturing companies were developing these MES applications.

In 2005, companies in process and batch manufacturing working within ISA95 decided to make their own diagrams and moved from “gears” to “oval eggs”.

The ANSI/ISA9-5 Standard for Enterprise-Control System Integration, made some nice clarifications and numbered the layers between machines and enterprise systems. For example, Layer 1 collects data in seconds, Layer 3 in hours, and Layer 4 in days.

However, they decided to rename the MES layer, Layer 3, to Manufacturing Operations Management (MOM) because they liked the word “Management” better than the word “Execution”. It was a naming preference, not a marker for an evolutionary step in manufacturing systems. It did not raise the bar for manufacturing systems; it was thought to be a better name.

You might actually see a few things in MES definitions which you don’t see in the MOM definition. The ISA95 standard was created by people working in the process and batch manufacturing industry where the manufacturing process is very equipment centric. In those industries, the process design is often the entire manufacturing plant or a fixed manufacturing cell with a flow designed to produce a recipe for a batch of product. In contrast, in discrete manufacturing the process design is more work order centric. The process is often designed for flexible manufacturing cells that can manufacture different types of configurations of product units for each work order. This is why there has always been a difference in how to model the different types of manufacturing. A major problem I have with the ISA95 standard is treating Quality as a separate silo. Quality is really part of every process. But I digress… back to discussions of the terms MES and MOM.

On the lighter side

I do think that the ISA95 team members underestimated how much we love acronyms! Because they might have thought twice about introducing the acronym “MOM”.

In this illustration you see an email I received about a week after the LinkedIn discussion had started with the title “MOMs email list”.

At first I thought, wow… I usually get a lot of unsolicited email offering PLM and ERP users email lists but this time it seemed like someone had an email list of MOM users. Sounded interesting so I opened the email to find out they were actually talking about email lists of Moms, Dads, Parents, Gamblers, Golfers, etc. I chuckled and pressed the delete button. This email illustrates a problem with the acronym MOM. Are we really going to ask the next generation to replace their old MOM and look for a new MOM? I find the MOM term a little hard to write about and use seriously in conversation about manufacturing systems.

A reality check

Is it worth it to transition to the term MOM to help align the discrete and process/batch camps? Even when the discrete manufacturing industries have done so much progress on the adoption of MES and establishment of COTS (commercial off the shelf) solutions for MES? Should the process/batch industries embrace MES even when they don’t feel they are “Executing” something? Google searches for “Manufacturing Execution System” were around 160 per month in 2008 and are around 480 per month in 2014. “Manufacturing Operations Management” searches are only at around 110 per month in 2014.

The move to a new term might be a setback for solutions focused at improving manufacturing operations. Two terms do not make it easier for companies to find the solutions they need. Confusion in the marketplace will only benefit the PLM and ERP software vendors that are trying to penetrate the manufacturing systems arena with broad and thin functionality. Perhaps these software giants are behind the move to change terms. Machiavellian tactics? Conspiracy paranoia?

Is there a third alternative?

Some are suggesting that we limit the use of the MOM term to discussions around the ISA95 model, discussions on “business processes” for Operations, and keep the MES term in relation to discussions about IT “systems” that help us support and execute those processes. Of course, that might start debates on how good or bad the ISA95 models are to represent discrete manufacturing processes versus process/batch manufacturing industries. That might be the topic of another article ; )

When MESA changed the “E” in MESA from “Execution” to “Enterprise” they were on to something. In discrete manufacturing, we are now developing manufacturing software for the extended enterprise. Driven by the reality that over half of our products’ subassemblies and components are manufactured by partners and suppliers.

Perhaps instead of focusing on the MES-MOM debate, we should focus on new terms for the future. Maybe change the “E” in MES and define the next generation of “Manufacturing Enterprise Systems” that will support better supplier collaboration and orchestration. Is it time for a four letter acronym? “eMES” for “Extended Manufacturing Enterprise Systems”? Some software vendors are already working on these solutions. It would be nice to have a market definition that supports a broader solution footprint for the broader problems manufacturers are trying to solve today.

Perhaps the term PPM (Production Process Management) or the term Collaborative Production Management (CPM used by ARC Group) will stick. I recommend everyone read the the paper by Michael McClellan titled “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”. It is an example of how we are now looking for tools to manage across the entire value chain.

My vote is to embrace a new term when we are ready to raise the bar for the software category. There is a need to define a new footprint that extends into the supply chain, not at a separate Layer 4, but as an integrated part of the manufacturing process. We should focus as architects of manufacturing systems on this next generation and what we are going to call it. We should look ahead, not back.

References

• “Computer Integrated Manufacturing”, Krieger Pub, Joseph Harrington, 1979.
• “Computer Integrated Manufacturing” by Cheng WU, Yushun Fan, Deyun Xiao, 1999
• “MES Explained – A High Level Vision”, MESA International, 1997
• “Applying Manufacturing Execution Systems” Michael McClellan, CRC Press/APICS, 1997
• ANSI/ISA9-5 “Standard for Enterprise-Control System Integration”, ISA, 2005
• “Improving Manufacturing Excellence by Managing Production Process across the Value Chain”, Michael McClellan, 2012

 

Moving forward with Manufacturing Apprenticeship Programs

Tognum, a Germany company with factories in South Carolina, moves forward with apprenticeship programs modeled after the ones in Germany in partnership between manufacturing companies, goverment and academia.

More of these programs are needed to fill the manufacturing skills gap as manufacturing companies start growing again and continue reshoring operations.

Read full article here:

http://mobile.nytimes.com/2013/12/01/business/where-factory-apprenticeship-is-latest-model-from-germany.html?_r=0&goback=%2Egde_3758755_member_5815870119015890947